



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class 					_snake_case      (       unittest.TestCase):




				def 				A__   (    self       :    List[str]    ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				@property
				def 				A__   (    self       :    List[str]    ):
							lowercase__       	=					1
							lowercase__       	=					3
							lowercase__       	=					(32, 32)

							lowercase__       	=					floats_tensor((batch_size, num_channels) + sizes,       rng=random.Random(0    )    ).to(lowercase_    )
							return image




				@property
				def 				A__   (    self       :    int    ):
							torch.manual_seed(0    )
							lowercase__       	=					UNetaDConditionModel(
							    block_out_channels=(32, 64),       layers_per_block=2,       sample_size=32,       in_channels=4,       out_channels=4,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),       cross_attention_dim=32,       )
							return model




				@property
				def 				A__   (    self       :    Optional[Any]    ):
							torch.manual_seed(0    )
							lowercase__       	=					AutoencoderKL(
							    block_out_channels=[32, 64],       in_channels=3,       out_channels=3,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],       latent_channels=4,       )
							return model




				@property
				def 				A__   (    self       :    List[str]    ):
							torch.manual_seed(0    )
							lowercase__       	=					RobertaSeriesConfig(
							    hidden_size=32,       project_dim=32,       intermediate_size=37,       layer_norm_eps=1e-0_5,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=5006,       )
							return RobertaSeriesModelWithTransformation(lowercase_    )




				@property
				def 				A__   (    self       :    str    ):







							def extract(*__lowercase       :    Optional[int],       **__lowercase       :    str    ):
										class 					_snake_case      :




														def __init__(    self       :    Dict    ):
																	lowercase__       	=					torch.ones([0]    )




														def 				A__   (    self       :    Union[str, Any],       __lowercase       :    Dict    ):
																	self.pixel_values.to(lowercase_    )
																	return self
										return Out()

							return extract




				def 				A__   (    self       :    Tuple    ):
							lowercase__       	=					"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowercase__       	=					self.dummy_cond_unet
							lowercase__       	=					PNDMScheduler(skip_prk_steps=lowercase_    )
							lowercase__       	=					self.dummy_vae
							lowercase__       	=					self.dummy_text_encoder
							lowercase__       	=					XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"    )
							lowercase__       	=					77

							lowercase__       	=					self.dummy_image.to(lowercase_    )
							lowercase__       	=					init_image / 2 + 0.5

							# make sure here that pndm scheduler skips prk
							lowercase__       	=					AltDiffusionImgaImgPipeline(
							    unet=lowercase_,       scheduler=lowercase_,       vae=lowercase_,       text_encoder=lowercase_,       tokenizer=lowercase_,       safety_checker=lowercase_,       feature_extractor=self.dummy_extractor,       )
							lowercase__       	=					VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,       do_normalize=lowercase_    )
							lowercase__       	=					alt_pipe.to(lowercase_    )
							alt_pipe.set_progress_bar_config(disable=lowercase_    )

							lowercase__       	=					"A painting of a squirrel eating a burger"
							lowercase__       	=					torch.Generator(device=lowercase_    ).manual_seed(0    )
							lowercase__       	=					alt_pipe(
							    [prompt],       generator=lowercase_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np",       image=lowercase_,       )

							lowercase__       	=					output.images

							lowercase__       	=					torch.Generator(device=lowercase_    ).manual_seed(0    )
							lowercase__       	=					alt_pipe(
							    [prompt],       generator=lowercase_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np",       image=lowercase_,       return_dict=lowercase_,       )[0]

							lowercase__       	=					image[0, -3:, -3:, -1]
							lowercase__       	=					image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowercase__       	=					np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 5e-3




				@unittest.skipIf(torch_device != "cuda",       "This test requires a GPU"    )
				def 				A__   (    self       :    Union[str, Any]    ):
							lowercase__       	=					self.dummy_cond_unet
							lowercase__       	=					PNDMScheduler(skip_prk_steps=lowercase_    )
							lowercase__       	=					self.dummy_vae
							lowercase__       	=					self.dummy_text_encoder
							lowercase__       	=					XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"    )
							lowercase__       	=					77

							lowercase__       	=					self.dummy_image.to(lowercase_    )

							# put models in fp16
							lowercase__       	=					unet.half()
							lowercase__       	=					vae.half()
							lowercase__       	=					bert.half()

							# make sure here that pndm scheduler skips prk
							lowercase__       	=					AltDiffusionImgaImgPipeline(
							    unet=lowercase_,       scheduler=lowercase_,       vae=lowercase_,       text_encoder=lowercase_,       tokenizer=lowercase_,       safety_checker=lowercase_,       feature_extractor=self.dummy_extractor,       )
							lowercase__       	=					VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,       do_normalize=lowercase_    )
							lowercase__       	=					alt_pipe.to(lowercase_    )
							alt_pipe.set_progress_bar_config(disable=lowercase_    )

							lowercase__       	=					"A painting of a squirrel eating a burger"
							lowercase__       	=					torch.manual_seed(0    )
							lowercase__       	=					alt_pipe(
							    [prompt],       generator=lowercase_,       num_inference_steps=2,       output_type="np",       image=lowercase_,       ).images

							assert image.shape == (1, 32, 32, 3)




				@unittest.skipIf(torch_device != "cuda",       "This test requires a GPU"    )
				def 				A__   (    self       :    int    ):
							lowercase__       	=					load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"    )
							# resize to resolution that is divisible by 8 but not 16 or 32
							lowercase__       	=					init_image.resize((760, 504)    )

							lowercase__       	=					"BAAI/AltDiffusion"
							lowercase__       	=					AltDiffusionImgaImgPipeline.from_pretrained(
							    lowercase_,       safety_checker=lowercase_,       )
							pipe.to(lowercase_    )
							pipe.set_progress_bar_config(disable=lowercase_    )
							pipe.enable_attention_slicing()

							lowercase__       	=					"A fantasy landscape, trending on artstation"

							lowercase__       	=					torch.manual_seed(0    )
							lowercase__       	=					pipe(
							    prompt=lowercase_,       image=lowercase_,       strength=0.75,       guidance_scale=7.5,       generator=lowercase_,       output_type="np",       )
							lowercase__       	=					output.images[0]

							lowercase__       	=					image[255:258, 383:386, -1]

							assert image.shape == (504, 760, 3)
							lowercase__       	=					np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
@slow
@require_torch_gpu
class 					_snake_case      (       unittest.TestCase):




				def 				A__   (    self       :    Dict    ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def 				A__   (    self       :    Optional[Any]    ):
							lowercase__       	=					load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"    )
							lowercase__       	=					init_image.resize((768, 512)    )
							lowercase__       	=					load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"    )

							lowercase__       	=					"BAAI/AltDiffusion"
							lowercase__       	=					AltDiffusionImgaImgPipeline.from_pretrained(
							    lowercase_,       safety_checker=lowercase_,       )
							pipe.to(lowercase_    )
							pipe.set_progress_bar_config(disable=lowercase_    )
							pipe.enable_attention_slicing()

							lowercase__       	=					"A fantasy landscape, trending on artstation"

							lowercase__       	=					torch.manual_seed(0    )
							lowercase__       	=					pipe(
							    prompt=lowercase_,       image=lowercase_,       strength=0.75,       guidance_scale=7.5,       generator=lowercase_,       output_type="np",       )
							lowercase__       	=					output.images[0]

							assert image.shape == (512, 768, 3)
							# img2img is flaky across GPUs even in fp32, so using MAE here
							assert np.abs(expected_image - image    ).max() < 1e-2





from __future__ import annotations

from collections import Counter
from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	None:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   str							, lowercase_				:   float)						->	None:


   """simple docstring"""







   if nodea not in self.connections:
     self.add_node(lowercase_)
   if nodea not in self.connections:
     self.add_node(lowercase_)
   _UpperCamelCase           =    probability







 def 		__UpperCAmelCase				(		self				:   Any)						->	list[str]:


   """simple docstring"""







   return list(self.connections)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    0
   _UpperCamelCase           =    random()

   for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
       return dest
   return ""


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->dict[str, int]:




  '''simple docstring'''

  _UpperCamelCase           =    MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(a__    ,     a__    ,     a__ )

  _UpperCamelCase           =    Counter(graph.get_nodes() )
  _UpperCamelCase           =    start

  for _ in range(a__ ):
    _UpperCamelCase           =    graph.transition(a__ )
    visited[node] += 1

  return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()




